Educational History

  • B.S. (Honours) Presidency College, Calcutta, India, 1994.
  • M.STAT. Indian Statistical Institute, Calcutta, India, 1996.
  • Ph.D. Statistics, University of Connecticut, Storrs, Connecticut, USA, 2000.

Research Interests

  • Statistical modeling and analysis of geographically referenced datasets.
  • Bayesian statistics (theory and methods) and hierarchical modelling.
  • Statistical computing and related software development.

Honors and awards

  • 2005, Inductee: Pi Chapter of Delta Omega National Honor Society.
  • 2009, Abdel El Sharaawi Young Researcher Award from The International Environmetrics Society.
  • 2010, Elected member, International Statistical Institute.
  • 2011, Mortimer Spiegelman Award from the Statistics Section of the American Public Health Association.
  • 2012, Elected Fellow of the American Statistical Association (ASA).
  • 2012, International Indian Statistical Association's Young Researcher Award.
  • 2015, Presidential Invited Address, Western North American Regional (WNAR) Meeting of the International Biometric Society.
  • 2015, Elected Fellow of the Institute of Mathematical Statistics (IMS).
  • 2015, Distinguished Achievement Medal from ASA Section on Statistics and the Environment.


Hierarchical Modeling and Analysis for Spatial Data. Second Edition

Keep up to date with the evolving landscape of space and space-time data analysis and modeling. Since the publication of the first edition, the statistical landscape has substantially changed for analyzing space and space-time data. More than twice the size of its predecessor, Hierarchical Modeling and Analysis for Spatial Data, Second Edition reflects the major growth in spatial statistics as both a research area and an area of application. → Details

Linear Algebra and Matrix Analysis for Statistics

Linear Algebra and Matrix Analysis for Statistics offers a gradual exposition to linear algebra without sacrificing the rigor of the subject. It presents both the vector space approach and the canonical forms in matrix theory. The book is as self-contained as possible, assuming no prior knowledge of linear algebra. The authors first address the rudimentary mechanics of linear systems using Gaussian elimination and the resulting decompositions. They introduce Euclidean vector spaces using less abstract concepts and make connections to systems of linear equations wherever possible. After illustrating the importance of the rank of a matrix, they discuss complementary subspaces, oblique projectors, orthogonality, orthogonal projections and projectors, and orthogonal reduction. The text then shows how the theoretical concepts developed are handy in analyzing solutions for linear systems. The authors also explain how determinants are useful for characterizing and deriving properties concerning matrices and linear systems. They then cover eigenvalues, eigenvectors, singular value decomposition, Jordan decomposition (including a proof), quadratic forms, and Kronecker and Hadamard products. The book concludes with accessible treatments of advanced topics, such as linear iterative systems, convergence of matrices, more general vector spaces, linear transformations, and Hilbert spaces. → Details

Selected publications

Datta, A., Banerjee, S., Finley, A.O., and Gelfand, A.E. (in press). Hierarchical nearest-neighbor Gaussian process models for large geostatistical datasets. Journal of the American Statistical Association. pdf (full text)

Quick, H., Banerjee, S. and Carlin, B.P. (2015). Bayesian modeling and analysis for gradients in spatiotemporal processes. Biometrics, 71, 575--584. pdf (full text) and supplementary material

Monteiro, J.V., Banerjee, S. and Ramachandran, G. (2014). Bayesian modeling for physical processes in industrial hygiene using misaligned workplace data. Technometrics, 56, 238-247.

Ren, Q. and Banerjee, S. (2013). Hierarchical factor models for large spatially misaligned datasets: A low-rank predictive process approach. Biometrics, 69, 19-30.

Quick, H., Banerjee, S. and Carlin, B.P. (2013). Modeling temporal gradients in regionally aggregated California asthma hospitalization data. Annals of Applied Statistics, 7, 154-176.

Finley, A.O., Banerjee, S. and MacFarlane, D.W. (2011). A hierarchical model for predicting forest variables over large heterogeneous domains. Journal of the American Statistical Association 106, 31-48.

Banerjee, S., Finley, A.O., Waldmann, P. and Ericcson, T. (2010). Hierarchical spatial process models for multiple traits in large genetic trials. Journal of the American Statistical Association, 105, 506-521.

Zhang, Y., Hodges, J.S. and Banerjee, S. (2009). Smoothed ANOVA with spatial effects as a competitor to MCAR in multivariate spatial smoothing. Annals of Applied Statistics 3, 1805-1830.

Finley, A.O., Banerjee, S. and McRoberts, R.E. (2009). Hierarchical spatial models for predicting tree species assemblages across large domains. Annals of Applied Statistics, 3, 1052-1079.

Banerjee, S., Gelfand, A.E., Finley, A.O. and Sang, H. (2008). Gaussian predictive process models for large spatial datasets. Journal of the Royal Statistical Society Series B, 70, 825--848.

Jin, X., Banerjee, S. and Carlin, B.P. (2007). Order-free coregionalized lattice models with application to multiple disease mapping. Journal of the Royal Statistical Society Series B, 69, 817-838.

Contact Information

Room 51-254B CHS
Department of Biostatistics
UCLA School of Public Health
Los Angeles, CA 90095-1772
Email: sudipto (at)
Phone: (310) 825-5916
Fax: (310) 267-2113

Upcoming and recent workshops

The Machine Learning Summer School, January 07-16, 2015, University of Texas, Austin

Short Course on Bayesian Spatio-Temporal Modeling → Details

Workshop on Spatial Statistics, January 29-31, 2015, Texas A&M University

Bayesian Modeling and Inference for Large Geographically Referenced Data Sets → Details

G70: A Celebration of Alan Gelfand's 70th Birthday, April 19-22, 2015, Duke University, Durham, NC

Wombling with Gelfand → Details

IMS/WNAR Meeting, June 14--17, 2015, Boise State University, Boise, Idaho

Presidential Invited Address: Statistics for Space, Time and Big Data → Details

40th Annual Summer Institute of Applied Statistics, June 17-19, 2015, Brigham Young University

Hierarchical Modeling and Analysis for Spatial Data. → Details